Deconstructing Carmona: The U.S. War on Drugs and Black Men as Non-Citizens

Article published in the VaLaw U.Law Review

Nonlocal effects on magnetism in the diluted magnetic semiconductor Ga_{1-x}Mn_{x}As

The magnetic properties of the diluted magnetic semiconductor Ga_{1-x}Mn_{x}As are studied within the dynamical cluster approximation. We use the k-dot-p Hamiltonian to describe the electronic structure of GaAs with spin-orbit coupling and strain effects. We show that nonlocal effects are essential for explaining the experimentally observed transition temperature and saturation magnetization. We also demonstrate that the cluster anisotropy is very strong and induces rotational frustration and a cube-edge direction magnetic anisotropy at low temperature. With this, we explain the temperature-driven spin reorientation in this system.Comment: 4 pages, 4 figures; to be published in Phys. Rev. Let

Deconstructing Carmona: The U.S. War on Drugs and Black Men as Non-Citizens

Article published in the VaLaw U.Law Review

Irradiance modelling for individual cells of shaded solar photovoltaic arrays

Developments in Photovoltaic (PV) design software have progressed to modelling the string or even the module as the smallest system unit but current methods lack computational efficiency to fully consider cell mismatch effects due to partial shading. This paper presents a more efficient shading loss algorithm which generates an irradiance map of the array for each time step for individual cells or cell portions. Irradiance losses are calculated from both near and far obstructions which might cause shading of both beam and diffuse irradiance in a three-dimensional reference field. The irradiance map output from this model could be used to calculate the performance of each solar cell individually as part of an overarching energy yield model. A validation demonstrates the calculation of shading losses due to a chimney with less than one percent error when compared with measured values

Characterization of increased persistence and intensity of precipitation in the northeastern United States

We present evidence of increasing persistence in daily precipitation in the northeastern United States that suggests that global circulation changes are affecting regional precipitation patterns. Meteorological data from 222 stations in 10 northeastern states are analyzed using Markov chain parameter estimates to demonstrate that a significant mode of precipitation variability is the persistence of precipitation events. We find that the largest region‐wide trend in wet persistence (i.e., the probability of precipitation in 1 day and given precipitation in the preceding day) occurs in June (+0.9% probability per decade over all stations). We also find that the study region is experiencing an increase in the magnitude of high‐intensity precipitation events. The largest increases in the 95th percentile of daily precipitation occurred in April with a trend of +0.7 mm/d/decade. We discuss the implications of the observed precipitation signals for watershed hydrology and flood risk

Compensation of temporal averaging bias in solar irradiance data

Solar irradiance data is used for the prediction of solar energy system performance but is presently a significant source of uncertainty in energy yield estimation. This also directly affects the expected revenue, so the irradiance uncertainty contributes to project risk and therefore the cost of finance. In this paper, the combined impact of temporal averaging, component deconstruction and plane translation mechanisms on uncertainty is analysed. A new method to redistribute (industry standard) hourly averaged data is proposed. This clearness index redistribution method is based on the statistical redistribution of clearness index values and largely corrects the bias error introduced by temporal averaging. Parameters for the redistribution model were derived using irradiance data measured at high temporal resolution by CREST, Loughborough University, over a 5 year period. The root mean square error (RMSE) of example net annual (2014) diffuse, beam and global yield of hourly averaged data were reduced from approximately 15% to 1%, 14% to 3% and 4% to 1%, respectively

Detection of roof shading for PV based on LiDAR data using a multi-modal approach

There is a current drive to increase rooftop deployment of PV. Suitable roofs need to be located, especially as regards shading. A shadow cast on one small section of a solar panel can disproportionately undermine output of the entire system. Nevertheless, few shading figures are available to researchers and developers. This paper reviews and categorizes a number of methods of determining shade losses on photovoltaic systems. Two existing methods are tested on case study areas: shadow simulation from buildings and ambient occlusion. The first is conceptually simple and was found to be useful where data is limited. The second is slightly more demanding in terms of data input and mathematical models. It produces attractive shadow maps but is intended for speed and represents an approximation to ray-tracing. Accordingly, a new model was developed which is fast, flexible and accurately models solar radiation

An analog approach for weather estimation using climate projections and reanalysis data

General circulation models (GCMs) are essential for projecting future climate; however, despite the rapid advances in their ability to simulate the climate system at increasing spatial resolution, GCMs cannot capture the local and regional weather dynamics necessary for climate impacts assessments. Temperature and precipitation, for which dense observational records are available, can be bias corrected and downscaled, but many climate impacts models require a larger set of variables such as relative humidity, cloud cover, wind speed and direction, and solar radiation. To address this need, we develop and demonstrate an analog-based approach, which we call a ‘‘weather estimator.’’ The weather estimator employs a highly generalizable structure, utilizing temperature and precipitation from previously downscaled GCMs to select analogs from a reanalysis product, resulting in a complete daily gridded dataset. The resulting dataset, constructed from the selected analogs, contains weather variables needed for impacts modeling that are physically, spatially, and temporally consistent. This approach relies on the weather variables’ correlation with temperature and precipitation, and our correlation analysis indicates that the weather estimator should best estimate evaporation, relative humidity, and cloud cover and do less well in estimating pressure and wind speed and direction. In addition, while the weather estimator has several user-defined parameters, a sensitivity analysis shows that the method is robust to small variations in important model parameters. The weather estimator recreates the historical distributions of relative humidity, pressure, evaporation, shortwave radiation, cloud cover, and wind speed well and outperforms a multiple linear regression estimator across all predictands

Clarifying the translational potential of B-I09

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